Hybrid SAST DAST Prioritization for Cloud Native Payment Service Vulnerabilities at Service Scale

Abstract

The rapid transition of financial infrastructure toward cloud-native architectures has fundamentally transformed the development, deployment, and maintenance of payment services. While microservices, containerization, and continuous integration methodologies offer unprecedented scalability and agility, they also introduce immense security challenges. Traditional security testing paradigms, such as standalone Static Application Security Testing and Dynamic Application Security Testing, frequently generate overwhelming volumes of vulnerability alerts, many of which are false positives or lack actionable context. This issue is particularly acute in large-scale payment services, where the regulatory environment demands rigorous compliance and the operational environment demands high availability. This paper presents a comprehensive framework for a hybrid vulnerability prioritization model that integrates static code analysis with dynamic runtime testing. By correlating the theoretical vulnerabilities identified during the static phase with the exploitable attack surfaces discovered during the dynamic phase, the proposed framework significantly reduces the noise associated with standalone testing methodologies. Furthermore, the methodology incorporates environmental context and business criticality into the risk scoring algorithm, providing security operations teams with a prioritized remediation queue that aligns with organizational risk tolerance. Through extensive theoretical modeling and empirical analysis within a simulated enterprise payment environment, this research demonstrates that the hybrid prioritization approach yields a substantial reduction in false positives and accelerates the mean time to remediation for critical vulnerabilities. The findings offer a scalable, automated solution for integrating robust security testing into the rapid release cycles characteristic of modern financial technology operations.